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These are some of the popular books and online courses where you need to start learning data science.  Books : “Python for Data Analysis” by Wes McKinney “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron “Introduction to Statistical Learning” by Gareth James et al. (free online version available) Online Courses: Coursera: “ Data Science Specialization ” by Johns Hopkins University, or “Machine Learning” by Andrew Ng. edX: Data Science MicroMasters Program (e.g., from UC San Diego). Kaggle Learn: Hands-on mini-courses on topics like Python, machine learning, and data visualization . Other tips to consider: Join LinkedIn groups or local meetups related to data science. Contribute to open-source projects on GitHub. Start applying for internships or junior roles, even if you have to start with less technical positions to build experience. Some of the key takeaways: Focus on core programming, statistics, and data manipulation first. Build a portfol...

 To secure a data science internship, you need a combination of foundational skills, practical experience, and strategic job-hunting. Here’s a step-by-step guide: 1. Build Your Foundation • Learn programming languages like Python and R . • Understand statistics, probability, and linear algebra. • Gain proficiency in data manipulation tools (e.g., SQL, Pandas, NumPy ) and visualization tools (e.g., Matplotlib, Tableau ). 2. Develop Key Data Science Skills • Master machine learning frameworks like Scikit-learn or TensorFlow . • Familiarize yourself with big data tools like Hadoop or Spark . • Learn to work with cloud platforms like AWS, Azure, or GCP. 3. Work on Projects • Build real-world projects using public datasets (e.g., Kaggle , UCI Repository ). • Create a portfolio on GitHub showcasing data cleaning, analysis, and predictive modeling. 4. Gain Certifications • Consider certifications in data science, such as: • Google Data Analytics...